On lightmyography based muscle-machine interfaces for the efficient decoding of human gestures and forces

Shahmohammadi M, Guan B, Godoy RV, Dwivedi A, Nielsen P, Liarokapis M (2023)

Publication Type: Journal article

Publication year: 2023

Journal

Scientific Reports Nature Publishing Group: Open Access Journals - Option B

Book Volume: 13

Article Number: 327

Journal Issue: 1

DOI: 10.1038/s41598-022-25982-w

Abstract

Conventional muscle-machine interfaces like Electromyography (EMG), have significant drawbacks, such as crosstalk, a non-linear relationship between the signal and the corresponding motion, and increased signal processing requirements. In this work, we introduce a new muscle-machine interfacing technique called lightmyography (LMG), that can be used to efficiently decode human hand gestures, motion, and forces from the detected contractions of the human muscles. LMG utilizes light propagation through elastic media and human tissue, measuring changes in light luminosity to detect muscle movement. Similar to forcemyography, LMG infers muscular contractions through tissue deformation and skin displacements. In this study, we look at how different characteristics of the light source and silicone medium affect the performance of LMG and we compare LMG and EMG based gesture decoding using various machine learning techniques. To do that, we design an armband equipped with five LMG modules, and we use it to collect the required LMG data. Three different machine learning methods are employed: Random Forests, Convolutional Neural Networks, and Temporal Multi-Channel Vision Transformers. The system has also been efficiently used in decoding the forces exerted during power grasping. The results demonstrate that LMG outperforms EMG for most methods and subjects.

Authors with CRIS profile

Anany Dwivedi Lehrstuhl für Autonome Systeme und Mechatronik

Involved external institutions

University of Auckland NZ New Zealand (NZ)

How to cite

APA:

Shahmohammadi, M., Guan, B., Godoy, R.V., Dwivedi, A., Nielsen, P., & Liarokapis, M. (2023). On lightmyography based muscle-machine interfaces for the efficient decoding of human gestures and forces. Scientific Reports, 13(1). https://dx.doi.org/10.1038/s41598-022-25982-w

MLA:

Shahmohammadi, Mojtaba, et al. "On lightmyography based muscle-machine interfaces for the efficient decoding of human gestures and forces." Scientific Reports 13.1 (2023).

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